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STAT 104  Intro To Statistics  Iowa State Study Resources

Lecture25
School: Iowa State
Course: Intro To Statistics
Stat 104 Lecture 25 Sampling Distributions Qualitative/Categorical variable Population Parameter: p known. Population Sample Distribution of Sample Proportion 1 Simulation Population Reeses Pieces statweb.calpoly.edu/chance/applets/Reeses/Reese

Exam 2 Fall 2011
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Exam 2 November 4, 2011 Name: _ INSTRUCTIONS: Read the questions carefully and completely. Answer each question and show work in the space provided. Partial credit will not be given if work is not shown. When asked to explain, describe, or commen

Exam 1 Fall 2011
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Exam 1 September 30, 2011 Name: _ INSTRUCTIONS: Read the questions carefully and completely. Answer each question and show work in the space provided. Partial credit will not be given if work is not shown. When asked to explain, describe, or comm

AF_Lecture15_S12
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 15 Binomial R. V. A special type of discrete random variable. Counts the number of successes in a series of independent trials. 1 Example Draw three times, with replacement, from the bag o chips. Count the number of times you win bonu

AF_Lecture11_F10
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 11 Controlling Cholesterol Does a higher dose of a new drug lower cholesterol more? 30 participants. Factor drug dose. Treatments: 10 mg or 20 mg. 15 subjects randomly assigned to each treatment. Response change in cholesterol. 1 Ex

AF_Lecture19_F13
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 19 Sampling Distributions Quantitative/Numerical variable Population Parameter: known. Population Sample Distribution of Sample Mean y 1 Example Population? Stat 104 students in this section. Variable? Number of children in your family.

AF_Lecture8_F13
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 8 Residuals Residuals help us see if the linear model makes sense. Plot residuals versus the explanatory variable. If the plot is a random scatter of points, then the linear model is the best we can do. 1 Plot of Residuals vs Body Mass

AF_Lecture20
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 20 1 Population Shape: Skewed right. Center: Mean, = 8.08 Spread: Standard Deviation, = 6.22 2 Distribution of the Sample Mean, y n=5 Shape: Approximately normal Center: Mean, = 8.08 Spread: Standard Deviation, 6.22 SD( y ) = = =

AF_Lecture9
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 9 Gathering Data Types of Studies Experimental Active manipulation. Observational Passive observation. 1 Experiment Subjects are assigned to experimental conditions and then outcomes on the response variable are recorded. Experimental

AF_Lecture7
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 7 Linear Regression Example: Body mass (kg) and Bite force (N) for Canidae. y, Response: Bite force (N) x, Explanatory: Body mass (kg) Cases: 28 species of Canidae. 1 Correlation Coefficient Body mass and Bite force zx z y r= n 1 = 2

AF_Lecture6
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 6 Correlation Linear Association How closely do the points on the scatter plot represent a straight line? The correlation coefficient gives the direction of and quantifies the strength of the linear association between two quantitative v

AF_Lecture3_F10
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 3 Summary Measures Central Tendency Sample midrange Sample median Sample mean 1 Measures of Center Sample Midrange Average of the minimum and the maximum. Body mass of Canidae: (1 + 36)/2=18.5 kilograms Greatly affected by outliers. 2 M

AF_Lecture4_F10
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 4 Sample Standard Deviation ( ( y y ) ) 2 s= n 1 1 Sample Variance Almost the average squared deviation ( (y y) ) = 2 s2 n 1 2 Squared Deviations 25 16 9 9 4 40 45 1 50 55 3 1 Stat 104 Lecture 4 Sample Variance: Golf Scores s2 = (16 + 9 +

AF_Lecture2_F10
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 2 Single Variable Data Categorical (Qualitative). Display Circle graph, bar graph, Pareto diagram. Numerical (Quantitative). Display dot plot, stem and leaf, histogram, box plot. Summary center, spread, position. 1 Data Who? Carnivo

Final Exam Fall 2011
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Final Exam December 13, 2011 Name: _ INSTRUCTIONS: Read the questions carefully and completely. Answer each question and show work in the space provided. Partial credit will not be given if work is not shown. When asked to explain, describe, or c

Lecture1
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 1 Course Objectives Develop Statistical Thinking. Display and summarize data. Evaluate probabilities. Use statistical methods to reach informed decisions. 1 Prerequisites Make sure you can do basic algebra. There will be a pretest in l

Lecture17
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 17 Normal Model 0.08 0.07 Density 0.06 0.05 0.04 0.03 0.02 0.01 0.00 40 45 50 55 60 65 70 75 80 Height (inches) 1 Normal Model Height Center: Mean, = 60 in. Spread: Standard deviation, = 6 in. 2 689599.7 Rule 68% of the values fall

Lecture24
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 24 Chapters 8 and 9 Quantitative variable Population Parameters: Population Inference Sample Sample Mean y 1 Example What is the mean alcohol content of beer? A random sample of 10 beers is taken and the alcohol content (%) is measured

Lecture23
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 23 Interpretation Getting a value of the sample proportion of 0.904 is very unusual if one were random sampling from a population with population proportion p = 0.94. The Pvalue is small, therefore reject Ho. 1 Conclusion Based on thi

Lecture25
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 25 t Distribution Confidence Level 80% 90% 95% 98% 99% 99.8% df 9 2.262 1 95% Confidence Interval y = 4.762 n = 10, df = (10 1) = 9 s = 0.314 t* = 2.262 2 Calculation s s y t* to y t * n n 0.314 0.314 4.762 2.262 to 4.762 2.

Lecture26
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 26 Another Example What is the mean heart rate for all young adults? Could the population mean heart rate for young adults be 70 beats per minute or is it something higher? 1 Sample Data Random sample of n = 25 young adults. Heart rat

Lecture28
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 28 Two Sample Data Independent samples Data are separate. Dependent samples Data are connected. 1 Independent Samples Two separate sets of individuals. One value of the response variable for each individual. 2 Dependent Samples Paire

Lecture27
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 27 Two Independent Samples Do males and females at I.S.U. spend the same amount of time, on average, at the Lied Recreation Athletic Center? 1 Populations random selection 2. Male Inference 1. Female Samples random selection 2 Time (minu

Lecture22
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 22 Margin of Error ME z * SE ( p) z * p(1 p) n The Margin of Error is the furthest p can be from p, for a given confidence. 1 Margin of Error ME z * SE ( p) z * Confidence z* 80% 90% p(1 p) n 95% 98% 99% 1.282 1.645 2 or 1.96 2.326 2.576

Lecture21
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 21 Parameter numerical summary of the entire population. Population all items of interest. Inference Sample a few items from the population. Statistic numerical summary of the sample. 1 Poll on the Environment Pew Research Center conduct

Lecture13
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 13 Special Addition Rule Addition Rule for disjoint events. P(A or B) = P(A) + P(B) P(First or Second) = P(First) + P(Second) = 329/2223 + 285/2223 = 614/2223 = 0.276 or 27.6% 1 General Addition Rule P(A or B) = P(A) + P(B) P(A and B)

Lecture12
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 12 Probability Subjective (Personal) Based on feeling or opinion. Empirical Based on experience. Theoretical (Formal) Based on assumptions. 1 The Deal Bag o chips (poker chips). Some are red. Some are white. Some are blue. Draw a chi

Lecture14
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 14 Probability Distributions Random variable Numerical values associated with the outcomes of a random phenomenon. 1 Probability Distributions Discrete random variable Numerical values associated with a distinct (discrete) set of point

Lecture16
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 16 Another Example 38% (p =0.38) of people in the United States have O+ blood type. If ten (n = 10) people come in to donate blood, what is the chance that 3 (x = 3) of them will have O+? 1 Example n = 10, p = 0.38, x = 3 10 P (3) =

Lecture18
School: Iowa State
Course: Introduction To Statistics
Stat 104 Lecture 18 Sampling Distribution The sampling distribution of a sample statistic displays the variation in repeated random samples from a given population. 1 Eye Color Population 1704 introductory statistics students. Parameter Proportion of t

AF_Lecture5
School: Iowa State
Course: Principles Of Statistics II
Stat 104 Lecture 5 Association Variables Response an outcome variable whose values exhibit variability. Explanatory a variable that we use to try to explain the variability in the response. 1 Association There is an association between two variables if